Predictive Model of Oxaliplatin-induced Liver Injury Based on Artificial Neural Network and Logistic Regression
© 2023 Authors..
Background and Aims: Identifying potential high-risk groups of oxaliplatin-induced liver injury (OILI) is valuable, but tools are lacking. So artificial neural network (ANN) and logistic regression (LR) models will be developed to predict the risk of OILI.
Methods: The medical information of patients treated with oxaliplatin between May and November 2016 at 10 hospitals was collected prospectively. We used the updated Roussel Uclaf causality assessment method (RUCAM) to identify cases of OILI and summarized the patient and medication characteristics. Furthermore, the ANN and LR models for predicting the risk of OILI were developed and evaluated.
Results: The incidence of OILI was 3.65%. The median RUCAM score with interquartile range was 6 (4, 9). The ANN model performed similarly to the LR model in sensitivity, specificity, and accuracy. In discrimination, the area under the curve of the ANN model was larger (0.920>0.833, p=0.019). In calibration, the ANN model was slightly improved. The important predictors of both models overlapped partially, including age, chemotherapy regimens and cycles, single and total dose of OXA, glucocorticoid drugs, and antihistamine drugs.
Conclusions: When the discriminative and calibration ability was given priority, the ANN model outperformed the LR model in predicting the risk of OILI. Other chemotherapy drugs in oxaliplatin-based chemotherapy regimens could have different degrees of impact on OILI. We suspected that OILI may be idiosyncratic, and chemotherapy dose factors may be weakly correlated. Decision making on prophylactic medications needs to be carefully considered, and the actual preventive effect needed to be supported by more evidence.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:11 |
|---|---|
| Enthalten in: |
Journal of clinical and translational hepatology - 11(2023), 7 vom: 28. Dez., Seite 1455-1464 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Huang, Rui [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Adverse drug reaction |
|---|
| Anmerkungen: |
Date Revised 03.01.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.14218/JCTH.2023.00399 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM366521330 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM366521330 | ||
| 003 | DE-627 | ||
| 005 | 20240108141342.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240108s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.14218/JCTH.2023.00399 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1247.xml |
| 035 | |a (DE-627)NLM366521330 | ||
| 035 | |a (NLM)38161498 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Huang, Rui |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Predictive Model of Oxaliplatin-induced Liver Injury Based on Artificial Neural Network and Logistic Regression |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 03.01.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2023 Authors. | ||
| 520 | |a Background and Aims: Identifying potential high-risk groups of oxaliplatin-induced liver injury (OILI) is valuable, but tools are lacking. So artificial neural network (ANN) and logistic regression (LR) models will be developed to predict the risk of OILI | ||
| 520 | |a Methods: The medical information of patients treated with oxaliplatin between May and November 2016 at 10 hospitals was collected prospectively. We used the updated Roussel Uclaf causality assessment method (RUCAM) to identify cases of OILI and summarized the patient and medication characteristics. Furthermore, the ANN and LR models for predicting the risk of OILI were developed and evaluated | ||
| 520 | |a Results: The incidence of OILI was 3.65%. The median RUCAM score with interquartile range was 6 (4, 9). The ANN model performed similarly to the LR model in sensitivity, specificity, and accuracy. In discrimination, the area under the curve of the ANN model was larger (0.920>0.833, p=0.019). In calibration, the ANN model was slightly improved. The important predictors of both models overlapped partially, including age, chemotherapy regimens and cycles, single and total dose of OXA, glucocorticoid drugs, and antihistamine drugs | ||
| 520 | |a Conclusions: When the discriminative and calibration ability was given priority, the ANN model outperformed the LR model in predicting the risk of OILI. Other chemotherapy drugs in oxaliplatin-based chemotherapy regimens could have different degrees of impact on OILI. We suspected that OILI may be idiosyncratic, and chemotherapy dose factors may be weakly correlated. Decision making on prophylactic medications needs to be carefully considered, and the actual preventive effect needed to be supported by more evidence | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Adverse drug reaction | |
| 650 | 4 | |a Liver injury | |
| 650 | 4 | |a Oxaliplatin | |
| 650 | 4 | |a Pharmacovigilance | |
| 650 | 4 | |a RUCAM | |
| 700 | 1 | |a Cai, Yuanxuan |e verfasserin |4 aut | |
| 700 | 1 | |a He, Yisheng |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Zaoqin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Tao |e verfasserin |4 aut | |
| 700 | 1 | |a Shangguan, Xiaofang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Yuhang |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Zherui |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yunzhou |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Chengliang |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of clinical and translational hepatology |d 2013 |g 11(2023), 7 vom: 28. Dez., Seite 1455-1464 |w (DE-627)NLM252610520 |x 2310-8819 |7 nnns |
| 773 | 1 | 8 | |g volume:11 |g year:2023 |g number:7 |g day:28 |g month:12 |g pages:1455-1464 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.14218/JCTH.2023.00399 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 11 |j 2023 |e 7 |b 28 |c 12 |h 1455-1464 | ||